Overview

Dataset statistics

Number of variables 13
Number of observations 9994
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 17
Duplicate rows (%) 0.2%
Total size in memory 1015.1 KiB
Average record size in memory 104.0 B

Variable types

Categorical 8
Numeric 5

Alerts

Country has constant value "United States" Constant
Dataset has 17 (0.2%) duplicate rows Duplicates
City has a high cardinality: 531 distinct values High cardinality
Sales is highly correlated with Profit High correlation
Discount is highly correlated with State and 3 other fields High correlation
Profit is highly correlated with Sales High correlation
Category is highly correlated with Sub-Category and 1 other fields High correlation
Segment is highly correlated with Country High correlation
Sub-Category is highly correlated with Category and 1 other fields High correlation
Ship Mode is highly correlated with Country High correlation
State is highly correlated with Postal Code and 2 other fields High correlation
Country is highly correlated with Category and 5 other fields High correlation
Region is highly correlated with State and 1 other fields High correlation
Postal Code is highly correlated with State and 2 other fields High correlation
Discount has 4798 (48.0%) zeros Zeros

Reproduction

Analysis started 2023-01-11 16:10:40.873915
Analysis finished 2023-01-11 16:10:48.817446
Duration 7.94 seconds
Software version pandas-profiling v3.3.0
Download configuration config.json

Variables

Ship Mode
Categorical

HIGH CORRELATION

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
Standard Class
5968 
Second Class
1945 
First Class
1538 
Same Day
 
543

Length

Max length 14
Median length 14
Mean length 12.82309386
Min length 8

Characters and Unicode

Total characters 128154
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Second Class
2nd row Second Class
3rd row Second Class
4th row Standard Class
5th row Standard Class

Common Values

Value Count Frequency (%)
Standard Class 5968
59.7%
Second Class 1945
 
19.5%
First Class 1538
 
15.4%
Same Day 543
 
5.4%

Length

2023-01-11T21:40:49.062017 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-11T21:40:49.183176 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Value Count Frequency (%)
class 9451
47.3%
standard 5968
29.9%
second 1945
 
9.7%
first 1538
 
7.7%
same 543
 
2.7%
day 543
 
2.7%

Most occurring characters

Value Count Frequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
l 9451
7.4%
C 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
r 7506
 
5.9%
t 7506
 
5.9%
Other values (8) 11083
8.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 98172
76.6%
Uppercase Letter 19988
 
15.6%
Space Separator 9994
 
7.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 22473
22.9%
s 20440
20.8%
d 13881
14.1%
l 9451
9.6%
n 7913
 
8.1%
r 7506
 
7.6%
t 7506
 
7.6%
e 2488
 
2.5%
c 1945
 
2.0%
o 1945
 
2.0%
Other values (3) 2624
 
2.7%
Uppercase Letter
Value Count Frequency (%)
C 9451
47.3%
S 8456
42.3%
F 1538
 
7.7%
D 543
 
2.7%
Space Separator
Value Count Frequency (%)
9994
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 118160
92.2%
Common 9994
 
7.8%

Most frequent character per script

Latin
Value Count Frequency (%)
a 22473
19.0%
s 20440
17.3%
d 13881
11.7%
l 9451
8.0%
C 9451
8.0%
S 8456
 
7.2%
n 7913
 
6.7%
r 7506
 
6.4%
t 7506
 
6.4%
e 2488
 
2.1%
Other values (7) 8595
 
7.3%
Common
Value Count Frequency (%)
9994
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 128154
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 22473
17.5%
s 20440
15.9%
d 13881
10.8%
9994
7.8%
l 9451
7.4%
C 9451
7.4%
S 8456
 
6.6%
n 7913
 
6.2%
r 7506
 
5.9%
t 7506
 
5.9%
Other values (8) 11083
8.6%

Segment
Categorical

HIGH CORRELATION

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
Consumer
5191 
Corporate
3020 
Home Office
1783 

Length

Max length 11
Median length 8
Mean length 8.837402441
Min length 8

Characters and Unicode

Total characters 88321
Distinct characters 17
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Consumer
2nd row Consumer
3rd row Corporate
4th row Consumer
5th row Consumer

Common Values

Value Count Frequency (%)
Consumer 5191
51.9%
Corporate 3020
30.2%
Home Office 1783
 
17.8%

Length

2023-01-11T21:40:49.291224 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-11T21:40:49.403137 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Value Count Frequency (%)
consumer 5191
44.1%
corporate 3020
25.6%
home 1783
 
15.1%
office 1783
 
15.1%

Most occurring characters

Value Count Frequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
n 5191
 
5.9%
s 5191
 
5.9%
u 5191
 
5.9%
f 3566
 
4.0%
t 3020
 
3.4%
Other values (7) 14955
16.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 74761
84.6%
Uppercase Letter 11777
 
13.3%
Space Separator 1783
 
2.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 13014
17.4%
e 11777
15.8%
r 11231
15.0%
m 6974
9.3%
n 5191
 
6.9%
s 5191
 
6.9%
u 5191
 
6.9%
f 3566
 
4.8%
t 3020
 
4.0%
p 3020
 
4.0%
Other values (3) 6586
8.8%
Uppercase Letter
Value Count Frequency (%)
C 8211
69.7%
H 1783
 
15.1%
O 1783
 
15.1%
Space Separator
Value Count Frequency (%)
1783
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 86538
98.0%
Common 1783
 
2.0%

Most frequent character per script

Latin
Value Count Frequency (%)
o 13014
15.0%
e 11777
13.6%
r 11231
13.0%
C 8211
9.5%
m 6974
8.1%
n 5191
 
6.0%
s 5191
 
6.0%
u 5191
 
6.0%
f 3566
 
4.1%
t 3020
 
3.5%
Other values (6) 13172
15.2%
Common
Value Count Frequency (%)
1783
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 88321
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
n 5191
 
5.9%
s 5191
 
5.9%
u 5191
 
5.9%
f 3566
 
4.0%
t 3020
 
3.4%
Other values (7) 14955
16.9%

Country
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct 1
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
United States
9994 

Length

Max length 13
Median length 13
Mean length 13
Min length 13

Characters and Unicode

Total characters 129922
Distinct characters 10
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row United States
2nd row United States
3rd row United States
4th row United States
5th row United States

Common Values

Value Count Frequency (%)
United States 9994
100.0%

Length

2023-01-11T21:40:49.491573 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-11T21:40:49.581757 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Value Count Frequency (%)
united 9994
50.0%
states 9994
50.0%

Most occurring characters

Value Count Frequency (%)
t 29982
23.1%
e 19988
15.4%
U 9994
 
7.7%
n 9994
 
7.7%
i 9994
 
7.7%
d 9994
 
7.7%
9994
 
7.7%
S 9994
 
7.7%
a 9994
 
7.7%
s 9994
 
7.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 99940
76.9%
Uppercase Letter 19988
 
15.4%
Space Separator 9994
 
7.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
t 29982
30.0%
e 19988
20.0%
n 9994
 
10.0%
i 9994
 
10.0%
d 9994
 
10.0%
a 9994
 
10.0%
s 9994
 
10.0%
Uppercase Letter
Value Count Frequency (%)
U 9994
50.0%
S 9994
50.0%
Space Separator
Value Count Frequency (%)
9994
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 119928
92.3%
Common 9994
 
7.7%

Most frequent character per script

Latin
Value Count Frequency (%)
t 29982
25.0%
e 19988
16.7%
U 9994
 
8.3%
n 9994
 
8.3%
i 9994
 
8.3%
d 9994
 
8.3%
S 9994
 
8.3%
a 9994
 
8.3%
s 9994
 
8.3%
Common
Value Count Frequency (%)
9994
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 129922
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
t 29982
23.1%
e 19988
15.4%
U 9994
 
7.7%
n 9994
 
7.7%
i 9994
 
7.7%
d 9994
 
7.7%
9994
 
7.7%
S 9994
 
7.7%
a 9994
 
7.7%
s 9994
 
7.7%

City
Categorical

HIGH CARDINALITY

Distinct 531
Distinct (%) 5.3%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
New York City
915 
Los Angeles
747 
Philadelphia
 
537
San Francisco
 
510
Seattle
 
428
Other values (526)
6857 

Length

Max length 17
Median length 14
Mean length 9.330698419
Min length 4

Characters and Unicode

Total characters 93251
Distinct characters 51
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 70 ?
Unique (%) 0.7%

Sample

1st row Henderson
2nd row Henderson
3rd row Los Angeles
4th row Fort Lauderdale
5th row Fort Lauderdale

Common Values

Value Count Frequency (%)
New York City 915
 
9.2%
Los Angeles 747
 
7.5%
Philadelphia 537
 
5.4%
San Francisco 510
 
5.1%
Seattle 428
 
4.3%
Houston 377
 
3.8%
Chicago 314
 
3.1%
Columbus 222
 
2.2%
San Diego 170
 
1.7%
Springfield 163
 
1.6%
Other values (521) 5611
56.1%

Length

2023-01-11T21:40:49.672362 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
city 994
 
7.0%
new 937
 
6.6%
york 920
 
6.5%
san 805
 
5.7%
los 747
 
5.2%
angeles 747
 
5.2%
philadelphia 537
 
3.8%
francisco 510
 
3.6%
seattle 428
 
3.0%
houston 377
 
2.6%
Other values (555) 7234
50.8%

Most occurring characters

Value Count Frequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 74773
80.2%
Uppercase Letter 14236
 
15.3%
Space Separator 4242
 
4.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 8719
11.7%
a 7591
10.2%
o 7499
10.0%
i 6229
 
8.3%
n 6199
 
8.3%
l 5986
 
8.0%
s 4699
 
6.3%
r 4468
 
6.0%
t 4438
 
5.9%
c 2393
 
3.2%
Other values (16) 16552
22.1%
Uppercase Letter
Value Count Frequency (%)
C 2085
14.6%
S 1740
12.2%
L 1295
9.1%
A 1242
8.7%
N 1134
8.0%
P 1013
 
7.1%
Y 940
 
6.6%
F 794
 
5.6%
D 627
 
4.4%
H 617
 
4.3%
Other values (14) 2749
19.3%
Space Separator
Value Count Frequency (%)
4242
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 89009
95.5%
Common 4242
 
4.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 8719
 
9.8%
a 7591
 
8.5%
o 7499
 
8.4%
i 6229
 
7.0%
n 6199
 
7.0%
l 5986
 
6.7%
s 4699
 
5.3%
r 4468
 
5.0%
t 4438
 
5.0%
c 2393
 
2.7%
Other values (40) 30788
34.6%
Common
Value Count Frequency (%)
4242
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 93251
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

State
Categorical

HIGH CORRELATION

Distinct 49
Distinct (%) 0.5%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
California
2001 
New York
1128 
Texas
985 
Pennsylvania
587 
Washington
506 
Other values (44)
4787 

Length

Max length 20
Median length 14
Mean length 8.487192315
Min length 4

Characters and Unicode

Total characters 84821
Distinct characters 46
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) < 0.1%

Sample

1st row Kentucky
2nd row Kentucky
3rd row California
4th row Florida
5th row Florida

Common Values

Value Count Frequency (%)
California 2001
20.0%
New York 1128
 
11.3%
Texas 985
 
9.9%
Pennsylvania 587
 
5.9%
Washington 506
 
5.1%
Illinois 492
 
4.9%
Ohio 469
 
4.7%
Florida 383
 
3.8%
Michigan 255
 
2.6%
North Carolina 249
 
2.5%
Other values (39) 2939
29.4%

Length

2023-01-11T21:40:49.797639 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
california 2001
17.1%
new 1322
 
11.3%
york 1128
 
9.6%
texas 985
 
8.4%
pennsylvania 587
 
5.0%
washington 506
 
4.3%
illinois 492
 
4.2%
ohio 469
 
4.0%
florida 383
 
3.3%
carolina 291
 
2.5%
Other values (43) 3542
30.3%

Most occurring characters

Value Count Frequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 71413
84.2%
Uppercase Letter 11696
 
13.8%
Space Separator 1712
 
2.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 10758
15.1%
i 9895
13.9%
n 8090
11.3%
o 7323
10.3%
r 5544
7.8%
e 5051
7.1%
l 4822
6.8%
s 4604
6.4%
f 2011
 
2.8%
h 1898
 
2.7%
Other values (14) 11417
16.0%
Uppercase Letter
Value Count Frequency (%)
C 2566
21.9%
N 1655
14.2%
T 1168
10.0%
Y 1128
9.6%
M 763
 
6.5%
I 748
 
6.4%
O 659
 
5.6%
W 621
 
5.3%
P 587
 
5.0%
F 383
 
3.3%
Other values (11) 1418
12.1%
Space Separator
Value Count Frequency (%)
1712
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 83109
98.0%
Common 1712
 
2.0%

Most frequent character per script

Latin
Value Count Frequency (%)
a 10758
12.9%
i 9895
11.9%
n 8090
 
9.7%
o 7323
 
8.8%
r 5544
 
6.7%
e 5051
 
6.1%
l 4822
 
5.8%
s 4604
 
5.5%
C 2566
 
3.1%
f 2011
 
2.4%
Other values (35) 22445
27.0%
Common
Value Count Frequency (%)
1712
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 84821
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

Postal Code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct 631
Distinct (%) 6.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 55190.37943
Minimum 1040
Maximum 99301
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 78.2 KiB
2023-01-11T21:40:49.925694 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum 1040
5-th percentile 10009
Q1 23223
median 56430.5
Q3 90008
95-th percentile 98006
Maximum 99301
Range 98261
Interquartile range (IQR) 66785

Descriptive statistics

Standard deviation 32063.69335
Coefficient of variation (CV) 0.5809652639
Kurtosis -1.493020228
Mean 55190.37943
Median Absolute Deviation (MAD) 33573.5
Skewness -0.1285255164
Sum 551572652
Variance 1028080431
Monotonicity Not monotonic
2023-01-11T21:40:50.073597 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
10035 263
 
2.6%
10024 230
 
2.3%
10009 229
 
2.3%
94122 203
 
2.0%
10011 193
 
1.9%
94110 166
 
1.7%
98105 165
 
1.7%
19134 160
 
1.6%
98103 151
 
1.5%
90049 151
 
1.5%
Other values (621) 8083
80.9%
Value Count Frequency (%)
1040 1
 
< 0.1%
1453 6
 
0.1%
1752 2
 
< 0.1%
1810 4
 
< 0.1%
1841 33
0.3%
1852 16
0.2%
1915 3
 
< 0.1%
2038 17
0.2%
2138 6
 
0.1%
2148 3
 
< 0.1%
Value Count Frequency (%)
99301 6
 
0.1%
99207 7
 
0.1%
98661 5
 
0.1%
98632 3
 
< 0.1%
98502 5
 
0.1%
98270 2
 
< 0.1%
98226 3
 
< 0.1%
98208 1
 
< 0.1%
98198 7
 
0.1%
98115 112
1.1%

Region
Categorical

HIGH CORRELATION

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
West
3203 
East
2848 
Central
2323 
South
1620 

Length

Max length 7
Median length 4
Mean length 4.859415649
Min length 4

Characters and Unicode

Total characters 48565
Distinct characters 14
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row South
2nd row South
3rd row West
4th row South
5th row South

Common Values

Value Count Frequency (%)
West 3203
32.0%
East 2848
28.5%
Central 2323
23.2%
South 1620
16.2%

Length

2023-01-11T21:40:50.201078 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-11T21:40:50.312423 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Value Count Frequency (%)
west 3203
32.0%
east 2848
28.5%
central 2323
23.2%
south 1620
16.2%

Most occurring characters

Value Count Frequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 38571
79.4%
Uppercase Letter 9994
 
20.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
t 9994
25.9%
s 6051
15.7%
e 5526
14.3%
a 5171
13.4%
n 2323
 
6.0%
r 2323
 
6.0%
l 2323
 
6.0%
o 1620
 
4.2%
u 1620
 
4.2%
h 1620
 
4.2%
Uppercase Letter
Value Count Frequency (%)
W 3203
32.0%
E 2848
28.5%
C 2323
23.2%
S 1620
16.2%

Most occurring scripts

Value Count Frequency (%)
Latin 48565
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 48565
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Category
Categorical

HIGH CORRELATION

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
Office Supplies
6026 
Furniture
2121 
Technology
1847 

Length

Max length 15
Median length 15
Mean length 12.80258155
Min length 9

Characters and Unicode

Total characters 127949
Distinct characters 20
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Furniture
2nd row Furniture
3rd row Office Supplies
4th row Furniture
5th row Office Supplies

Common Values

Value Count Frequency (%)
Office Supplies 6026
60.3%
Furniture 2121
 
21.2%
Technology 1847
 
18.5%

Length

2023-01-11T21:40:50.408563 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-11T21:40:50.523395 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Value Count Frequency (%)
office 6026
37.6%
supplies 6026
37.6%
furniture 2121
 
13.2%
technology 1847
 
11.5%

Most occurring characters

Value Count Frequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
s 6026
 
4.7%
S 6026
 
4.7%
Other values (10) 29560
23.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 105903
82.8%
Uppercase Letter 16020
 
12.5%
Space Separator 6026
 
4.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 16020
15.1%
i 14173
13.4%
p 12052
11.4%
f 12052
11.4%
u 10268
9.7%
c 7873
7.4%
l 7873
7.4%
s 6026
 
5.7%
r 4242
 
4.0%
n 3968
 
3.7%
Other values (5) 11356
10.7%
Uppercase Letter
Value Count Frequency (%)
O 6026
37.6%
S 6026
37.6%
F 2121
 
13.2%
T 1847
 
11.5%
Space Separator
Value Count Frequency (%)
6026
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 121923
95.3%
Common 6026
 
4.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 16020
13.1%
i 14173
11.6%
p 12052
9.9%
f 12052
9.9%
u 10268
8.4%
c 7873
 
6.5%
l 7873
 
6.5%
O 6026
 
4.9%
s 6026
 
4.9%
S 6026
 
4.9%
Other values (9) 23534
19.3%
Common
Value Count Frequency (%)
6026
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 127949
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
s 6026
 
4.7%
S 6026
 
4.7%
Other values (10) 29560
23.1%

Sub-Category
Categorical

HIGH CORRELATION

Distinct 17
Distinct (%) 0.2%
Missing 0
Missing (%) 0.0%
Memory size 78.2 KiB
Binders
1523 
Paper
1370 
Furnishings
957 
Phones
889 
Storage
846 
Other values (12)
4409 

Length

Max length 11
Median length 9
Mean length 7.191715029
Min length 3

Characters and Unicode

Total characters 71874
Distinct characters 28
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Bookcases
2nd row Chairs
3rd row Labels
4th row Tables
5th row Storage

Common Values

Value Count Frequency (%)
Binders 1523
15.2%
Paper 1370
13.7%
Furnishings 957
9.6%
Phones 889
8.9%
Storage 846
8.5%
Art 796
8.0%
Accessories 775
7.8%
Chairs 617
6.2%
Appliances 466
 
4.7%
Labels 364
 
3.6%
Other values (7) 1391
13.9%

Length

2023-01-11T21:40:50.642220 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
binders 1523
15.2%
paper 1370
13.7%
furnishings 957
9.6%
phones 889
8.9%
storage 846
8.5%
art 796
8.0%
accessories 775
7.8%
chairs 617
6.2%
appliances 466
 
4.7%
labels 364
 
3.6%
Other values (7) 1391
13.9%

Most occurring characters

Value Count Frequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 61880
86.1%
Uppercase Letter 9994
 
13.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
s 9934
16.1%
e 8870
14.3%
r 7169
11.6%
i 5668
9.2%
n 5378
8.7%
a 4542
7.3%
o 3288
 
5.3%
p 3004
 
4.9%
h 2578
 
4.2%
c 2359
 
3.8%
Other values (8) 9090
14.7%
Uppercase Letter
Value Count Frequency (%)
P 2259
22.6%
A 2037
20.4%
B 1751
17.5%
F 1174
11.7%
S 1036
10.4%
C 685
 
6.9%
L 364
 
3.6%
T 319
 
3.2%
E 254
 
2.5%
M 115
 
1.2%

Most occurring scripts

Value Count Frequency (%)
Latin 71874
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 71874
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Sales
Real number (ℝ≥0)

HIGH CORRELATION

Distinct 5825
Distinct (%) 58.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 229.8580008
Minimum 0.444
Maximum 22638.48
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 78.2 KiB
2023-01-11T21:40:50.783585 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum 0.444
5-th percentile 4.98
Q1 17.28
median 54.49
Q3 209.94
95-th percentile 956.984245
Maximum 22638.48
Range 22638.036
Interquartile range (IQR) 192.66

Descriptive statistics

Standard deviation 623.2451005
Coefficient of variation (CV) 2.711435313
Kurtosis 305.3117532
Mean 229.8580008
Median Absolute Deviation (MAD) 45.406
Skewness 12.97275234
Sum 2297200.86
Variance 388434.4553
Monotonicity Not monotonic
2023-01-11T21:40:50.916033 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
12.96 56
 
0.6%
19.44 39
 
0.4%
15.552 39
 
0.4%
25.92 36
 
0.4%
10.368 36
 
0.4%
32.4 28
 
0.3%
17.94 21
 
0.2%
6.48 21
 
0.2%
20.736 19
 
0.2%
14.94 17
 
0.2%
Other values (5815) 9682
96.9%
Value Count Frequency (%)
0.444 1
 
< 0.1%
0.556 1
 
< 0.1%
0.836 1
 
< 0.1%
0.852 1
 
< 0.1%
0.876 1
 
< 0.1%
0.898 1
 
< 0.1%
0.984 1
 
< 0.1%
0.99 1
 
< 0.1%
1.044 1
 
< 0.1%
1.08 3
< 0.1%
Value Count Frequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.976 1
< 0.1%

Quantity
Real number (ℝ≥0)

Distinct 14
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.789573744
Minimum 1
Maximum 14
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 78.2 KiB
2023-01-11T21:40:51.023274 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 2
median 3
Q3 5
95-th percentile 8
Maximum 14
Range 13
Interquartile range (IQR) 3

Descriptive statistics

Standard deviation 2.225109691
Coefficient of variation (CV) 0.5871662201
Kurtosis 1.991889366
Mean 3.789573744
Median Absolute Deviation (MAD) 1
Skewness 1.278544753
Sum 37873
Variance 4.951113138
Monotonicity Not monotonic
2023-01-11T21:40:51.124217 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
Value Count Frequency (%)
3 2409
24.1%
2 2402
24.0%
5 1230
12.3%
4 1191
11.9%
1 899
 
9.0%
7 606
 
6.1%
6 572
 
5.7%
9 258
 
2.6%
8 257
 
2.6%
10 57
 
0.6%
Other values (4) 113
 
1.1%
Value Count Frequency (%)
1 899
 
9.0%
2 2402
24.0%
3 2409
24.1%
4 1191
11.9%
5 1230
12.3%
6 572
 
5.7%
7 606
 
6.1%
8 257
 
2.6%
9 258
 
2.6%
10 57
 
0.6%
Value Count Frequency (%)
14 29
 
0.3%
13 27
 
0.3%
12 23
 
0.2%
11 34
 
0.3%
10 57
 
0.6%
9 258
 
2.6%
8 257
 
2.6%
7 606
6.1%
6 572
5.7%
5 1230
12.3%

Discount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct 12
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.1562027216
Minimum 0
Maximum 0.8
Zeros 4798
Zeros (%) 48.0%
Negative 0
Negative (%) 0.0%
Memory size 78.2 KiB
2023-01-11T21:40:51.219724 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 0.2
Q3 0.2
95-th percentile 0.7
Maximum 0.8
Range 0.8
Interquartile range (IQR) 0.2

Descriptive statistics

Standard deviation 0.2064519678
Coefficient of variation (CV) 1.321692514
Kurtosis 2.409546123
Mean 0.1562027216
Median Absolute Deviation (MAD) 0.2
Skewness 1.684294747
Sum 1561.09
Variance 0.04262241502
Monotonicity Not monotonic
2023-01-11T21:40:51.310335 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Value Count Frequency (%)
0 4798
48.0%
0.2 3657
36.6%
0.7 418
 
4.2%
0.8 300
 
3.0%
0.3 227
 
2.3%
0.4 206
 
2.1%
0.6 138
 
1.4%
0.1 94
 
0.9%
0.5 66
 
0.7%
0.15 52
 
0.5%
Other values (2) 38
 
0.4%
Value Count Frequency (%)
0 4798
48.0%
0.1 94
 
0.9%
0.15 52
 
0.5%
0.2 3657
36.6%
0.3 227
 
2.3%
0.32 27
 
0.3%
0.4 206
 
2.1%
0.45 11
 
0.1%
0.5 66
 
0.7%
0.6 138
 
1.4%
Value Count Frequency (%)
0.8 300
 
3.0%
0.7 418
 
4.2%
0.6 138
 
1.4%
0.5 66
 
0.7%
0.45 11
 
0.1%
0.4 206
 
2.1%
0.32 27
 
0.3%
0.3 227
 
2.3%
0.2 3657
36.6%
0.15 52
 
0.5%

Profit
Real number (ℝ)

HIGH CORRELATION

Distinct 7287
Distinct (%) 72.9%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 28.65689631
Minimum -6599.978
Maximum 8399.976
Zeros 65
Zeros (%) 0.7%
Negative 1871
Negative (%) 18.7%
Memory size 78.2 KiB
2023-01-11T21:40:51.433252 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum -6599.978
5-th percentile -53.03092
Q1 1.72875
median 8.6665
Q3 29.364
95-th percentile 168.4704
Maximum 8399.976
Range 14999.954
Interquartile range (IQR) 27.63525

Descriptive statistics

Standard deviation 234.2601077
Coefficient of variation (CV) 8.174650359
Kurtosis 397.1885146
Mean 28.65689631
Median Absolute Deviation (MAD) 10.77855
Skewness 7.561431562
Sum 286397.0217
Variance 54877.79806
Monotonicity Not monotonic
2023-01-11T21:40:51.562266 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 65
 
0.7%
6.2208 43
 
0.4%
9.3312 38
 
0.4%
5.4432 32
 
0.3%
3.6288 32
 
0.3%
15.552 26
 
0.3%
12.4416 21
 
0.2%
7.2576 19
 
0.2%
3.1104 18
 
0.2%
9.072 11
 
0.1%
Other values (7277) 9689
96.9%
Value Count Frequency (%)
-6599.978 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2639.9912 1
< 0.1%
-2287.782 1
< 0.1%
-1862.3124 1
< 0.1%
-1850.9464 1
< 0.1%
-1811.0784 1
< 0.1%
Value Count Frequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2799.984 1
< 0.1%
2591.9568 1
< 0.1%
2504.2216 1
< 0.1%

Interactions

2023-01-11T21:40:47.619636 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.060109 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.733308 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.342671 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.964089 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:47.751873 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.229644 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.845783 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.477559 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:47.125347 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:47.873147 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.351921 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.949142 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.597996 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:47.244121 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:48.010432 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.478395 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.069534 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.717495 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:47.362473 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:48.156718 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:45.604132 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.210356 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:46.842825 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
2023-01-11T21:40:47.488258 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-11T21:40:51.683414 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-11T21:40:51.805071 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-11T21:40:52.095333 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-11T21:40:52.221397 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-11T21:40:52.362723 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-11T21:40:48.378404 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-11T21:40:48.682447 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Ship Mode Segment Country City State Postal Code Region Category Sub-Category Sales Quantity Discount Profit
0 Second Class Consumer United States Henderson Kentucky 42420 South Furniture Bookcases 261.9600 2 0.00 41.9136
1 Second Class Consumer United States Henderson Kentucky 42420 South Furniture Chairs 731.9400 3 0.00 219.5820
2 Second Class Corporate United States Los Angeles California 90036 West Office Supplies Labels 14.6200 2 0.00 6.8714
3 Standard Class Consumer United States Fort Lauderdale Florida 33311 South Furniture Tables 957.5775 5 0.45 -383.0310
4 Standard Class Consumer United States Fort Lauderdale Florida 33311 South Office Supplies Storage 22.3680 2 0.20 2.5164
5 Standard Class Consumer United States Los Angeles California 90032 West Furniture Furnishings 48.8600 7 0.00 14.1694
6 Standard Class Consumer United States Los Angeles California 90032 West Office Supplies Art 7.2800 4 0.00 1.9656
7 Standard Class Consumer United States Los Angeles California 90032 West Technology Phones 907.1520 6 0.20 90.7152
8 Standard Class Consumer United States Los Angeles California 90032 West Office Supplies Binders 18.5040 3 0.20 5.7825
9 Standard Class Consumer United States Los Angeles California 90032 West Office Supplies Appliances 114.9000 5 0.00 34.4700

Last rows

Ship Mode Segment Country City State Postal Code Region Category Sub-Category Sales Quantity Discount Profit
9984 Standard Class Consumer United States Long Beach New York 11561 East Office Supplies Labels 31.500 10 0.0 15.1200
9985 Standard Class Consumer United States Long Beach New York 11561 East Office Supplies Supplies 55.600 4 0.0 16.1240
9986 Standard Class Consumer United States Los Angeles California 90008 West Technology Accessories 36.240 1 0.0 15.2208
9987 Standard Class Corporate United States Athens Georgia 30605 South Technology Accessories 79.990 1 0.0 28.7964
9988 Standard Class Corporate United States Athens Georgia 30605 South Technology Phones 206.100 5 0.0 55.6470
9989 Second Class Consumer United States Miami Florida 33180 South Furniture Furnishings 25.248 3 0.2 4.1028
9990 Standard Class Consumer United States Costa Mesa California 92627 West Furniture Furnishings 91.960 2 0.0 15.6332
9991 Standard Class Consumer United States Costa Mesa California 92627 West Technology Phones 258.576 2 0.2 19.3932
9992 Standard Class Consumer United States Costa Mesa California 92627 West Office Supplies Paper 29.600 4 0.0 13.3200
9993 Second Class Consumer United States Westminster California 92683 West Office Supplies Appliances 243.160 2 0.0 72.9480

Duplicate rows

Most frequently occurring

Ship Mode Segment Country City State Postal Code Region Category Sub-Category Sales Quantity Discount Profit # duplicates
0 First Class Consumer United States Houston Texas 77041 Central Office Supplies Paper 47.952 3 0.2 16.1838 2
1 Same Day Home Office United States San Francisco California 94122 West Office Supplies Labels 41.400 4 0.0 19.8720 2
2 Second Class Consumer United States Seattle Washington 98115 West Office Supplies Paper 12.960 2 0.0 6.2208 2
3 Second Class Corporate United States Chicago Illinois 60653 Central Office Supplies Binders 3.564 3 0.8 -6.2370 2
4 Standard Class Consumer United States Detroit Michigan 48227 Central Furniture Chairs 389.970 3 0.0 35.0973 2
5 Standard Class Consumer United States Los Angeles California 90036 West Office Supplies Paper 19.440 3 0.0 9.3312 2
6 Standard Class Consumer United States New York City New York 10011 East Office Supplies Paper 49.120 4 0.0 23.0864 2
7 Standard Class Consumer United States Salem Oregon 97301 West Office Supplies Paper 10.368 2 0.2 3.6288 2
8 Standard Class Consumer United States San Francisco California 94122 West Office Supplies Paper 12.840 3 0.0 5.7780 2
9 Standard Class Consumer United States San Francisco California 94122 West Office Supplies Paper 25.920 4 0.0 12.4416 2